Introduction

Across all ages and genders, the 3 forms of cancer with the highest number of incidences in NHS Borders between 1997-2021 are:

Cancer Site / Incidences

  1. Non-melanoma skin cancer / 6174
  2. Basal cell carcinoma of the skin / 4049
  3. Breast / 2614

This report will focus on instances of breast cancer, which among females in NHS Borders, has the highest number of incidences, highest mean crude rate and highest mean European age-standardised rate (EASR).

Cancer Site / Incidences

1. Breast / 2598
2. Non-melanoma skin cancer / 2519
3. Basal cell carcinoma of the skin / 1882

Cancer Site / Mean EASR

1. Breast / 161.3640
2. Non-melanoma skin cancer / 150.3996
3. Basal cell carcinoma of the skin / 113.9178

Health Board Comparison

To understand how these rates compare to other health boards in Scotland, we can visualise a five year summary of the EASR.

geo_summary %>% 
  ggplot(aes(fill = easr, text = paste0("<b>NHS Health Board:</b> ", HBName, "<br>", 
                             "<b>EASR:</b> ", easr, "<br>"))) + 
  geom_sf(colour = "white", linewidth = 0.04) +
  labs(
    title = "5 Year Summary (2017-2021): Female Breast Cancer EASR",
    subtitle = "By NHS Health Board",
    fill = "EASR") +
  scale_fill_distiller(palette = "Blues", direction = +1) +
  theme(panel.background = element_rect(fill = "white"),
        axis.text.x = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks = element_blank(),
        rect = element_blank(),
        axis.title.x = element_blank(),
        axis.title.y = element_blank())

five_year_summary %>%
  select(hb, cancer_site, sex, year, easr) %>%
  filter(sex == "Females",
         cancer_site == "Breast",
         hb != "GR0800001") %>%
  left_join(geography_codes, "hb") %>% 
  select(hb_name, year, sex, cancer_site, easr) %>% 
  arrange(desc(easr)) %>% 
  gt() %>%
  cols_label(
    hb_name = "NHS Health Board",
    year = "Year(s)",
    sex = "Sex",
    cancer_site = "Cancer Site",
    easr = "EASR") %>% 
  tab_options(column_labels.font.weight = 'bold') %>%
  data_color(columns = easr, palette = "Blues")
NHS Health Board Year(s) Sex Cancer Site EASR
NHS Dumfries and Galloway 2017-2021 Females Breast 174.6153
NHS Lothian 2017-2021 Females Breast 172.3179
NHS Forth Valley 2017-2021 Females Breast 171.6585
NHS Lanarkshire 2017-2021 Females Breast 169.1486
NHS Greater Glasgow and Clyde 2017-2021 Females Breast 168.8007
NHS Borders 2017-2021 Females Breast 164.8136
NHS Fife 2017-2021 Females Breast 164.4207
NHS Tayside 2017-2021 Females Breast 163.4222
NHS Highland 2017-2021 Females Breast 162.5039
NHS Ayrshire and Arran 2017-2021 Females Breast 157.0019
NHS Grampian 2017-2021 Females Breast 156.2987

Hypothesis Test

cancer_incidence_borders_sample <- cancer_incidence_borders %>%
  filter(sex == "Females", cancer_site == "Breast") %>%
  select(id, cancer_site, sex, year, incidences_all_ages) %>% 
    mutate(peak = case_when(
    year == 1999 ~ "peak",
    year == 2002 ~ "peak",
    year == 2005 ~ "peak",
    year == 2008 ~ "peak",
    year == 2011 ~ "peak",
    year == 2014 ~ "peak",
    year == 2017 ~ "peak",
    TRUE          ~ "standard"
    )
  )

observed_stat <- cancer_incidence_borders_sample %>% 
  specify(incidences_all_ages ~ peak) %>%
  calculate(stat = "diff in means", order = c("peak", "standard"))

null_distribution <- cancer_incidence_borders_sample %>% 
  specify(response = incidences_all_ages, explanatory = peak) %>%
  hypothesize(null = "independence") %>%
  generate(reps = 1000, type = "permute") %>% 
  calculate(stat = "diff in means", order = c("peak", "standard"))

p_value <- null_distribution %>%
  get_p_value(obs_stat = observed_stat, direction = "right")

What does hypothesis testing tell us?

Question: Is the mean number of female breast cancer incidences in “peak years” (1999, 2002, 2005, 2008, 2011, 2014, 2017) greater than mean number of female breast cancer incidences in “non-peak years” (1997, 1998, 2000, 2001, 2003, 2006, 2007, 2009, 2010, 2012, 2013, 2015, 2016, 2018, 2019)?

Test Used: Two Sample Mean Test (Independent)
Significance Level: 0.05

H0: \(\mu{1}\) - \(\mu{2}\) = 0
H1: \(\mu{1}\) - \(\mu{2}\) > 0

Result:

Based on a bootstrapped NULL distribution, a very low p-value which is less than 0.05 is returned. We therefor reject H0 in favor of H1 with evidence suggesting that there is a statistically significant increase in the mean number of female breast cancer incidences in “peak years”.

Why might there be a historic 3 year trend?

Women who meet screening criteria are invited for breast screening once every 3 years (NHS National Services Scotland, 2022).

Why might we not see the same peak in 2020 as we may have expected?

Due to the COVID-19 pandemic, no invites to breast screenings were sent between 30 March 2020 and 3 August 2020 (Public Health Scotland, 2022).

Incidences by Age

[fig. 2]

fig2_plot <- five_year_summary_long %>%
  filter(cancer_site == "Breast",
         sex == "Females") %>%
  ggplot() +
  geom_col(aes(x = age, y = incidences, 
               text = paste0("<b>Age:</b> ", age, "<br>", "<b>Incidences:</b> ", incidences, "<br>")),
           fill = "#0391BF") +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5)) +
  labs(
    x = "\n Age",
    y = "Incidences\n",
    title = "Total Female Breast Cancer Incidences by Age") +
  theme(panel.background = element_rect(fill = "white"),
        panel.grid = element_line(colour = "grey90"))

ggplotly(fig2_plot, tooltip = "text") %>%
  layout(title = list(text = paste0("<b>Total Female Breast Cancer Incidences by Age</b>",
                                    "<br>",
                                    "<sup>",
                                    "NHS Borders: 1997-2021",
                                    "</sup>")))

What does this visualisation tell us?

  • The majority of breast cancer incidences in females appear to be between those aged between 50 and 79.

Why might these age groups see increased incidence numbers?

  • Currently only women between the ages of 50 and 70 are routinely screened (NHS National Services Scotland, 2022).

NHS Borders Population Projections:

Females 50+ 2021: 29889

Females 50+ 2041: 31148 (4.21225% increase)

(National Records of Scotland, 2023)

Conclusions / Recommendations

  • Screening data should be reviewed to establish if the resulting back-log from COVID-19 has been cleared in order to establish whether a further increase in incidences should be anticipated in 2022.

  • Resources should be allocated according to the observed trend of increased incidences every three years

  • Research/Analysis should be conducted to further understand and confirm any reason for this trend, including any links to screening schedules.

  • Research/Analysis should be conducted to establish whether increased incidence with age is in any way the result of current screening criteria and if therefor screening criteria should be widened.

  • Long term service planning should take into consideration the ~4% projected population increase of the female 50-70 demographic in NHS Borders, as rejected by the National Records of Scotland.

Data Sources